AlisQI MCP for AI. Orchestrate your entire QMS data flow via conversation.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
AlisQI connects your AI agent directly to your Quality Management System (QMS) for full data auditing and result orchestration. You can list, retrieve, and store quality results, check metadata definitions across analysis sets, and monitor webhooks—all through natural conversation.
This MCP lets you manage complex compliance data without leaving your chat interface.
What your AI can do
List analysis sets
Provides a list of every configured analysis set within your account.
Get analysis set details
Retrieves specific metadata for a defined analysis set.
List results
Finds and lists quality results, supporting filtering by various criteria.
Retrieve and analyze specific quality results or list all available analysis sets in your system.
List and audit the field definitions within any analysis set to understand exactly what data is being tracked.
Get technical metadata for attached documents, helping you track quality documentation integrity.
List active webhooks to confirm that critical events, like detected non-conformities, are correctly triggering downstream systems.
Create or modify quality results directly through the chat interface for immediate compliance logging.
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AlisQI MCP: 10 Tools for Quality Management
These ten tools allow your agent to perform every function required in quality control, from auditing results to discovering data structure.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using AlisQI on VinkiusList Analysis Sets
Provides a list of every configured analysis set within your account.
Get Analysis Set Details
Retrieves specific metadata for a defined analysis set.
List Results
Finds and lists quality results, supporting filtering by various criteria.
Get Result Details
Retrieves the full details of one specific quality result entry.
Store Results
Creates or updates a specific quality result entry in the system.
List Fields
Lists all dynamic fields defined within an analysis set to understand the schema.
List Choice Lists
Retrieves the available options from selection menus used in quality data entry.
Get Result Attachments
Lists and provides technical metadata for documents attached to a quality result.
List Active Webhooks
Lists all currently active system triggers or webhooks for monitoring events.
Get Api Info
Checks the current operational status of the API connection.
Security and governance baked right in.
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Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Start with AlisQI, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 10 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Manually auditing quality results is slow and error-prone.
Right now, checking the full compliance history for a product batch means logging into multiple interfaces. You pull up the analysis set definition in one tab, then open another to look at the actual result entries, and maybe a third just to find the supporting document metadata. It’s clicks, copy-pasting URLs, and cross-referencing dates.
With this MCP, you don't do any of that. You ask your agent, 'Give me all results for lot X.' It handles the complex queries across the QMS system and gives you a single, readable report detailing everything needed.
Getting full visibility into quality data definitions with `list_fields`
Previously, if an engineer wanted to know what fields were available for the 'Raw Material Inspection' set, they had to guess or wait for someone else to tell them. They’d waste time assuming certain metadata was present when it wasn't.
Now, a simple prompt calls `list_fields`, and your agent immediately returns a structured list of every single dynamic field available. You know the schema before you write a line of code or enter data.
What your AI can actually do with this
Manually auditing technical reports or tracing a specific field's definition can be a headache. AlisQI gives your AI agent the keys to your entire quality operation. You stop copying and pasting between tabs; instead, you simply ask questions like, “Show me all results for lot 45 that failed moisture testing.” Your agent handles the query, pulls the data, and presents it in plain language.
It lets you look up technical metadata for fields, list every active webhook monitoring non-conformities, or even write new quality records if they've been manually verified. This level of operational visibility is huge. When you connect AlisQI via Vinkius, your agent doesn't just read data; it orchestrates the entire workflow—from schema discovery to final result storage—all from one place.
019d754c-42cb-723f-9eac-bfcf4b31345a Here's how it actually works
The bottom line is you talk to it like a teammate and get structured quality data back instantly.
First, subscribe to this MCP and provide your AlisQI Instance URL and Bearer Token.
Next, your AI client uses these credentials to connect to the quality management API.
After connecting, you can immediately use natural language prompts to ask questions about results, metadata, or workflow status.
Who is this actually for?
This connector is for operations leads, compliance officers, and lab technicians. It's for anyone who gets bogged down tracing audit trails or confirming if the right technical metadata exists before signing off on a product batch.
Runs audits on historical data by asking the agent to list quality results and check trends across multiple analysis sets.
Looks up specific requirements or enters new compliance data directly through chat, verifying that all necessary fields are present.
Inspects the underlying API responses by listing dynamic fields and tracing how quality data flows from one set to another.
What Changes When You Connect
Instead of manually navigating through tabs to see available audit templates, you can use list_analysis_sets to get a full list instantly. This saves time when starting a new compliance check.
When you need to know what fields are available for data entry, running list_fields tells you the exact schema without having to consult documentation or guess at API endpoints.
If a critical process needs an automated alert (like a failed non-conformity), use list_active_webhooks to confirm that the trigger is set up and sending data correctly.
Need to audit records? You can run list_results, using filters to narrow down thousands of entries, then pull specific details with get_result_details. It’s powerful filtering without the spreadsheet work.
When a result needs correcting or logging, you don't need another system. Use store_results directly through your agent to write and update data while maintaining a clear audit trail.
See it in action
Auditing Material Batch Compliance
A Quality Manager needs to check if all environmental monitoring reports for the last quarter passed inspection. They ask their agent, which uses list_results and then get_result_details, pulling up every necessary entry so they can sign off quickly.
Investigating a Schema Discrepancy
A Data Engineer notices that the 'Batch ID' field is missing from a new set of reports. They use get_analysis_set_details and then cross-reference it with list_fields to confirm if the metadata definition was updated correctly.
Confirming Workflow Alerts
An Operations Lead wants assurance that when a temperature deviation happens, an alert gets sent immediately. They run list_active_webhooks to verify the webhook ID and check if it's pointing to the right incident management system.
Logging Immediate Corrective Action
A Lab Technician finds a minor deviation during testing. Instead of filling out paper forms, they use store_results through their agent, logging the exact details and attaching relevant metadata in minutes.
The honest tradeoffs
Treating results like simple records
Trying to manually extract a result's attachment URL from one screen, then copying it into a spreadsheet, and finally updating the record in another system.
Use get_result_attachments first. This pulls the technical metadata for all documents associated with a result, letting your agent gather every link and detail you need in one go.
Ignoring schema changes
Assuming that because a field worked last month, it still contains required data this month. The system might break when the definition changes.
Before processing any data, run list_fields and check the output against your current requirements. This confirms the dynamic data model is stable.
Overlooking active triggers
Thinking that just because a process exists doesn't mean it works when an error occurs, leading to blind spots in compliance reporting.
Always check list_active_webhooks. This confirms the system is configured not just with triggers, but that they are actively running and sending data.
When It Fits, When It Doesn't
Use this MCP if your process requires auditing structured quality data, tracking compliance metadata, or managing complex audit trails. You need to know why a result was generated and what the schema is. Don't use it if you just need basic messaging between people; that’s for simple communication tools. If all you need is to write a single piece of text into a database without any structured metadata checks, then a general data writing tool might be enough. But when quality control and compliance are involved, this MCP ensures every step—from listing sets (list_analysis_sets) to storing results (store_results)—is traceable.
Questions you might have
How do I check if my webhooks are working with the `list_active_webhooks` tool? +
You simply ask your agent to list active webhooks. It confirms which ID is running and whether there are any reported errors, letting you know instantly if your non-conformity triggers are live.
Can I use `list_results` to find specific data points? +
Yes. You tell the agent what criteria you're looking for, and it uses advanced filtering on list_results. It doesn't just give you a list; it narrows down complex datasets based on your input.
What is the difference between `get_analysis_set_details` and `list_analysis_sets`? +
Use list_analysis_sets to see all available sets in the system. Use get_analysis_set_details when you want deep, specific metadata about one single set.
I need to update a result. Which tool should I use? Is it `store_results`? +
Yes, store_results is the correct tool for creating or updating entries. You just tell your agent which fields you're changing and what the new values are.
How do I find out what documents relate to a result using `get_result_attachments`? +
You provide the specific result ID, and the tool returns all technical metadata for every attached document. This helps you track compliance documentation without manually checking folders.
I need to understand my data structure; what does `list_fields` show me? +
It provides a complete list of all dynamic fields available in your system. This is crucial for auditing your full data model, showing you every possible field definition that quality results can contain.
Before running any reports, how do I check the API connection status using `get_api_info`? +
This tool confirms the current operational status of your AlisQI instance. Use it to verify connectivity and ensure all core services are accessible through the MCP before initiating data retrieval or write operations.
If I want to audit standardized vocabularies, how do I use `list_choice_lists`? +
It retrieves a list of every predefined choice menu used in your quality reports. This helps you verify the controlled vocabulary—like material grades or test types—ensuring data consistency across all records.
How do I find my AlisQI Bearer Token? +
Log in to AlisQI, navigate to Menu > Management > Integration Hub. You can generate and manage your API tokens there. Each token inherits the permissions of the associated user account.
Is the data model the same for everyone? +
No, AlisQI uses a dynamic, user-defined data model. This means field names and analysis sets are specific to your company's configuration. Use the list_analysis_sets tool to discover your unique structure.
Can I attach files to quality results via the agent? +
Currently, the agent can retrieve metadata for existing attachments using the get_result_attachments tool. For uploading new files, we recommend using the AlisQI web interface or specific integration endpoints.
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